Multi-instance model fitting has been studied since the early sixties.
The Hough-transform [1]}, [2]} perhaps is the first popular method for finding multiple instances of a single class [3]}, [4]}, [5]}, [6]}.
The RANSAC [7]} algorithm was as well extended to deal with finding multiple instances.
Sequential RANSAC [8]}, [9]} detects instances in a sequential manner by repeatedly running RANSAC to recover a single instance and, then, removing its inliers from the point set.
The greedy approach that makes RANSAC a powerful tool for recovering a single instance becomes its drawback when estimating multiple ones.
Points are assigned not to the best but to the first instance, typically the one with the largest support, for which they cannot be deemed outliers.
MultiRANSAC [10]} forms compound hypotheses about \(n\)  instances.
In each iteration, MultiRANSAC draws samples of size \(n\)  times \(m\) , where \(m\)  is the number of points required for estimating a model instance, , \(m = 4\)  for homographies.
Besides requiring the number \(n\)  of the instances to be known a priori,
the increased sample size affects the problem complexity and, thus, the processing time severely.
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